A Strategic Foresight Framework for New Product Development Based on Scientific, Technological, and Market Trend Analysis

Document Type : مقاله مستخرج از رساله دکتری

Authors

1 Strategy and Business Policy, Faculty of Management, University of Tehran, Tehran, Iran

2 Political Science, Faculty of Law and Political Science, University of Tehran, Tehran, Iran

3 Marketing and Market Development, Faculty of Management, University of Tehran, Tehran, Iran

Abstract

Aim and introduction: The perfume industry, standing as a quintessential segment of the Fast-Moving Consumer Goods (FMCG) market, is characterized by high volatility, rapid shifts in consumer preferences, and a fiercely competitive landscape where the product failure rate reaches approximately 45%. This sector faces a unique challenge: it must balance artistic creativity with scientific rigor and fluctuating market trends. Traditional market research methods, which are often retrospective or heavily focused on current explicit needs, fail to capture the complex, emerging forces shaping the future. Consequently, existing literature reveals a significant gap: the lack of an integrated framework that simultaneously analyzes scientific, technological, and market trends to guide New Product Development (NPD). Current approaches tend to analyze these domains in isolation—creating "islands" of insight that miss the synergistic opportunities at their intersection. This study aims to bridge this gap by proposing a Strategic Foresight Framework. The primary objective is to move beyond fragmented trend analysis and provide a holistic mechanism that translates macro-trends into concrete, de-risked, and innovative product concepts, using the perfume industry as a case study for managing uncertainty in creative, experience-driven sectors.
Methodology: To achieve a comprehensive foresight analysis, this research employs an explanatory sequential mixed-methods design, executed in three distinct phases that move from quantitative data mining to qualitative expert synthesis. Phase One utilized rigorous quantitative techniques to map macro-trends across three domains. In the Science domain, 3,103 articles from the Web of Science were analyzed via bibliometrics, network analysis, theme analysis, and topic modeling to identify shifting research priorities. In the Technology domain, 19,646 patents from Lens.org were scrutinized using CPC classification network analysis, Subject-Action-Object (SAO) semantic analysis, and topic modeling to decode functional innovations. The Market domain analysis focused on the top 100 rated female perfumes annually from 2012 to 2024, sourced from Parfumo.net, utilizing product feature network analysis, association rule mining to find hidden ingredient combinations, dynamic link prediction, and sentiment analysis to understand consumer "whys." Phase Two involved a qualitative expert panel of 30 specialists, including foresight experts, technical perfumers, and market analysts. Through structural analysis and cross-impact matrices, the diverse quantitative data points were synthesized to identify key "Driving Forces" and their interdependencies. Finally, Phase Three employed a three-round Delphi method to refine these drivers into a validated "Future Roadmap" and subsequently generate and validate a specific new product concept, ensuring the findings were not only theoretical but also operationally viable.
Finding: The integrated analysis revealed a paradigmatic shift in the perfume industry, moving from a transactional model focused on selling "products" to a relational model centered on offering "experience-based ecosystems."
In the scientific domain, bibliometric analysis highlighted a shift from the traditional "motor theme" of chemical synthesis and formulation toward emerging themes such as "Neuroscience" and "Sensory Perception." Research is increasingly focusing on the biological and psychological impact of scent, validating the connection between olfactory stimuli and emotional well-being.
In the technological domain, the SAO analysis revealed a functional evolution from "preservation" to "active control." Innovation is currently driven by "Smart Encapsulation" systems designed for triggered release in response to moisture or heat, rather than just stability. Furthermore, the analysis identified a surge in AI-driven formulation and "Green Chemistry/Biogenesis," indicating a move away from resource-intensive extraction toward precision molecular design that creates nature-identical scents through fermentation.
In the market domain, quantitative mining of perfume notes has revealed a specific evolution in consumer palates: the "Gourmand" (edible) notes are aggressively infiltrating all levels of the olfactory pyramid, moving from base notes to top notes. Association rule mining has identified that successful modern perfumes increasingly pair traditional floral hearts with unexpected savory or woody accords to create greater complexity. Furthermore, sentiment analysis indicates that consumers are shifting from simply seeking a "good scent" to demanding "self-expression" and "functional well-being," with a non-negotiable emphasis on sustainability.
Synthesizing these streams, the study identified five converging macro-drivers for the future: (1) Functional Scent: Perfumes designed to perform specific tasks, such as enhancing mood or malodor neutralization. (2) Personalized: A shift toward "My Scent" rather than "The Scent," enabled by AI and layering techniques. (3) Conscious: A demand for radical transparency, where biotechnology replaces scarce natural resources to ensure sustainability without compromising quality. (4) Experience-Driven: Scent as a narrative device, integrated with digital storytelling. (5) Reinvented: The use of AI to discover novel molecules that human noses have never encountered.
Based on these drivers, the expert panel validated a final product concept titled "The Alchemist." This concept features a refillable, sustainable vessel and a transforming scent profile utilizing notes such as Sichuan pepper (representing energy/transformation) and Oud (representing depth/permanence). It is designed to provide a tangible narrative of personal transformation, satisfying the strategic need for a product that is simultaneously functional, deeply personal, and sustainably engineered.
Discussion and Coclusion: This study contributes to the innovation management literature by demonstrating that integrating strategic foresight into the early "fuzzy front end" of new product development (NPD) significantly reduces uncertainty and failure rates. The proposed framework validates that successful future products must address three strategic imperatives: multi-modal functionality (combining olfactory pleasure with psychological benefits), deep emotional personalization (moving beyond demographic targeting to psychographic resonance), and tangible sustainability (embedding ethics into the product's molecular construction). By systematically linking scientific capabilities, technological enablers, and market demands, the framework provides a robust roadmap for organizations to navigate volatility and actively shape the future of their industry.

Keywords


غمخواری، سیده معصومه؛ ملایی، الهه؛ رسولی، نسرین و ترابی، محمدامین. (۱۳۹۹). بررسی نقش میانجی نوگرایی سازمانی و نقش تعدیل‌گر پویایی بازار بر رابطه قابلیت حسگری بازار و عملکرد شرکت. راهبردهای بازرگانی، (17)16. 1-14. https://doi.org/10.22070/cs.2021.13834.1056
کاظمی سراسکانرود، زهرا؛ شیرخدایی، میثم؛ یحیی‌زاده‌فر، محمود؛ صفری، محمد و نامدار طجری، سمیه. (۱۴۰۲). واکاوی نقش کمپین‌های تبلیغاتی زیست‌محیطی در ترویج رفتارهای شهروندی زیست‌محیطی: ارائه چارچوب به‌کارگیری بازاریابی اجتماعی. راهبردهای بازرگانی، ۲۰(۲۱)، ۱۸۱-۲۱۰. https://doi.org/10.22070/cs.2024.19115.1380
کشاورز، محمدرضا. (1402). ارائه مدل کارکردی هوش مصنوعی و یادگیری ماشین در بازاریابی عصبی. نشریه علمی راهبردهای بازرگانی. 20(22). 47-64. https://doi.org/10.22070/cs.2024.19182.1383
Alshaer, S. A. (2023). A SEM-Artificial Neural Network Analysis to Examine the Role of Strategic Foresight on Organizational Success. Journal of Intelligence Studies in Business, 13(3), 55–70.
Baxter, D., & Turner, N. (2023). Why Scrum works in new product development: the role of social capital in managing complexity. Production Planning & Control, 34(13), 1248–1260.
Biercewicz, K., Chrąchol-Barczyk, U., Duda, J., & Wiścicka-Fernando, M. (2022). Modern methods of sustainable behaviour analysis the case of purchasing FMCG. Sustainability, 14(20), 13387.
Calof, J., & Colton, B. (2024). Developing foresight that impacts senior management decisions. Technological Forecasting and Social Change, 198, 122957.
Charter, M., & Tischner, U. (2017). Sustainable solutions: Developing products and services for the future. Routledge. https://doi.org/10.9774/gleaf.978-1-907643-21-7_10
Chen, H., Tsang, Y. P., & Wu, C. H. (2023). When text mining meets science mapping in the bibliometric analysis: A review and future opportunities. International Journal of Engineering Business Management, 15. https://doi.org/10.1177/18479790231222349
Cooper, R. G. (2011). Winning at new products: Creating value through innovation. Basic Books. https://www.google.com/books/edition/Winning_at_New_Products/5GAfqqJwnPQC?hl=en&gbpv=0
Del Vecchio, P., Mele, G., Passiante, G., & Serra, D. (2023). Knowledge generation from Big Data for new product development: a structured literature review. Knowledge Management Research & Practice, 21(4), 892-907. https://doi.org/10.1080/14778238.2022.2094292
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.
Faiella, A., & Corazza, G. E. (2025). Cognitive mechanisms in foresight: A bridge between psychology and futures studies. Futures, 166, 102496. https://doi.org/10.1016/j.futures.2025.103547
Gaviria, M., & Kilic, B. (2021). A network analysis of COVID-19 mRNA vaccine patents. Nature biotechnology, 39(5), 546-548. https://doi.org/10.1038/s41587-021-00912-9
Ghamkhari, S. M., Mollaei, E., Rasouli, N., & Torabi, M. A. (2021). The mediating role of organizational modernity and the moderator role of market dynamics on the relationship between market sensibility and firm performance. Business Strategies, 17(16), 1–14. https://doi.org/10.22070/cs.2021.13834.1056. [In Persian]
He, P., Pei, Y., Lin, C., & Ye, D. (2021). Ambidextrous marketing capabilities, exploratory and exploitative market-based innovation, and innovation performance: an empirical study on China's manufacturing sector. Sustainability, 13(3), 1146. https://doi.org/10.3390/su13031146
He, A. Z., & Zhang, Y. (2023). AI-powered touch points in the customer journey: a systematic literature review and research agenda. Journal of Research in Interactive Marketing, 17(4), 620-639. https://doi.org/10.1108/JRIM-03-2022-0082
Hiltunen, E. (2008). The future sign and its three dimensions. Futures40(3), 247-260.
Hussain, Z., & Khan, A. (2024). Investigating the Determinants and Consumer Preferences of Sustainable Consumption and Production Adoption Among Fast-Moving Consumer Goods Manufacturers. In Sustainable Development Goals: The Impact of Sustainability Measures on Wellbeing (Vol. 113, pp. 79-92). Emerald Publishing Limited. https://doi.org/10.1108/S1569-37592024000113A005
Iqbal, A., Bhat, M. A., Muneeb, Q., & Javid, M. (2025). Revolutionizing perfume creation: PTD's innovative approach. Digital Chemical Engineering, 15, 100223. https://doi.org/10.1016/j.dche.2025.100223
Jang, H. J., Park, S. J., & Yoon, B. (2023). Exploring Technology Opportunities Based on User Needs: Application of Opinion Mining and SAO Analysis. Engineering Management Journal, 35(3), 209–222. https://doi.org/10.1080/10429247.2022.2050130
Kazemi Saraskanrood, Z., Shirkhodaei, M., Yahyazadehfar, M., Safari, M., & Namdar Tajari, S. (2024). Analyzing the role of environmental advertising campaigns in promoting environmental citizenship behaviors: Providing a framework for applying social marketing. Business Strategies20(21), 181–210. https://doi.org/10.22070/cs.2024.19115.1380. [In Persian]
Keshavarz, M. (2024). Presenting a functional model of artificial intelligence and machine learning in neuromarketing. Business Strategies, 20(22), 47–64. https://doi.org/10.22070/cs.2024.19182.1383. [In Persian]
Khan, H., Zahoor, N., Gerged, A. M., Tarba, S., & Makrides, A. (2022). The efficacy of market sensing and family-controlled board in the new product development performance of family firms in emerging market. Journal of Business Research, 141, 673–684. https://doi.org/10.1016/j.jbusres.2021.11.064
Lee, C., Jeon, D., Ahn, J. M., & Kwon, O. (2020). Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database. Technovation, 96, 102140. https://doi.org/10.1016/j.technovation.2020.102140
Li, X., Sarpong, D., & Wang, C. L. (2022). Collaborative Strategic Foresight and New Product Development in Chinese Pharmaceutical Firms. IEEE Transactions on Engineering Management, 69(2), 551-563. https://doi.org/10.1109/TEM.2020.3040041
Malacina, I., & Teplov, R. (2022). Supply chain innovation research: A bibliometric network analysis and literature review. International Journal of Production Economics, 251, 108540.
Marzi, G. (2022). On the nature, origins and outcomes of Over Featuring in the new product development process. Journal of Engineering and Technology Management, 64.
Moreno, E. M. G., Bolaño, I. M., & Rodriguez, Y. E. L. (2024). A bibliometric analysis of the convergence between strategic planning and foresight with innovation management. Innovaciencia, 12(1). https://doi.org/10.15649/2346075X.3355
Mubarak, M. F., & Jucevicius, G. (2025). Strategic foresight, knowledge management, and open innovation: Drivers of new product development success. Journal of Innovation & Knowledge, 10.(2). https://doi.org/10.1016/j.jik.2025.100654
Okada, Y., Kishita, Y., Nomaguchi, Y., Yano, T., & Ohtomi, K. (2022). Backcasting-Based Method for Designing Roadmaps to Achieve a Sustainable Future. IEEE Transactions on Engineering Management, 69(1), 168-178. https://doi.org/10.1109/TEM.2020.3008444
Olszewski, M. (2022). Boosting creativity in co-creation with consumers in the fuzzy front-end of new product development: A literature review and organising framework. e-mentor, (2), 36–47. https://www.doi.org/10.15219/em94.1563
Ou, C. C., & Chuang, H. H. (2023). Exploring the Factors that Influence Consumers to Purchase Perfume Products. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 8(5), 18. https://dialnet.unirioja.es/servlet/articulo?codigo=8956941
Ozcan, S., Homayounfard, A., Simms, C., & Wasim, J. (2022). Technology Roadmapping Using Text Mining: A Foresight Study for the Retail Industry. IEEE Transactions on Engineering Management, 69(1), 228–244. https://doi.org/10.1109/TEM.2021.3068310
Rohrbeck, R., & Kum, M. (2018). Corporate foresight and its impact on firm performance: A longitudinal analysis. Technological Forecasting and Social Change, 129, 105-116.
Rohrbeck, R., & Schwarz, J. O. (2013). The value contribution of strategic foresight: Insights from an empirical study of large European companies. Technological Forecasting and Social Change, 80(8), 1593–1606. https://doi.org/10.1016/j.techfore.2013.01.004
Rutkowski, I. P. (2022). Success and failure rates of new food and non-food products introduced on the market. Journal of Marketing and Consumer Behaviour in Emerging Markets, 14(1), 52-61. https://www.ceeol.com/search/article-detail?id=1081343
Sakellariou, E., & Vecchiato, R. (2022). Foresight, sensemaking, and new product development: Constructing meanings for the future. Technological Forecasting and Social Change, 184. https://doi.org/10.1016/j.techfore.2022.121945
Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126(6), 5113-5142. https://doi.org/10.1007/s11192-021-03948-5
Sytnik, V. M., & Proskuryakova, L. N. (2024). Expanding foresight methodology to better understand the unknown future and identify hard-to-pred ict events. European Journal of Futures Research, 12(1). https://doi.org/10.1186/s40309-024-00244-2
Tian, B., Fu, J., & Li, C. (2024). Determinants of competitive advantage: The roles of innovation orientation, fuzzy front end, and internal competition. R & D Management, 54(1), 21-38.
Tueanrat, Y., Papagiannidis, S., & Alamanos, E. (2021). Going on a journey: A review of the customer journey literature. Journal of business research, 125, 336-353.
Vaismoradi, M., & Snelgrove, S. (2019). Theme in Qualitative Content Analysis and Thematic Analysis. Forum Qualitative Sozialforschung Forum: Qualitative Social Research, 20(3).
Yin, J., Jiang, P., & Mahdiraji, H. (2024). Congruence and Discrepancy: Matching Effect of Searching and Integration on Green Product Development Performance. Sustainability, 16(20).
Zhao, H., Liu, Z., Yao, X., & Yang, Q. (2021). A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach. Information Processing & Management, 58(5), 102656. https://doi.org/10.1016/j.ipm.2021.102656